Oropharyngeal Dysphagia: A Proposal for an Ecological Theoretical Model

Background: The exponential growth in epidemiological studies has been reflected in an increase in analytical studies. Thus, theoretical models are required to guide the definition of data analysis, although so far, they are seldom used in Speech, Language, and Hearing Sciences. Objective: To propose a multicausal model for oropharyngeal dysphagia using directed acyclic graphs showing mediating variables, confounding variables, and variables connected by direct causation. Design: This integrative literature review. Setting: This was carried out until January 4, 2021, and searches were performed with the MEDLINE, EMBASE,and other bases.

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CAUSAL ANALYSIS AFTER HAAVELMO

Econometric Theory ◽

10.1017/s026646661400022x ◽

2014 ◽

Vol 31 (1) ◽

pp. 115-151 ◽

Cited By ~ 25

Author(s):

James Heckman ◽

Rodrigo Pinto

Keyword(s):

Economic Models ◽

Causal Analysis ◽

Directed Acyclic Graphs ◽

Ceteris Paribus ◽

Acyclic Graphs ◽

Rigorous Treatment ◽

Definition Of

Haavelmo’s seminal 1943 and 1944 papers are the first rigorous treatment of causality. In them, he distinguished the definition of causal parameters from their identification. He showed that causal parameters are defined usinghypotheticalmodels that assign variation to some of the inputs determining outcomes while holding all other inputs fixed. He thus formalized and made operational Marshall’s (1890)ceteris paribusanalysis. We embed Haavelmo’s framework into the recursive framework of Directed Acyclic Graphs (DAGs) commonly used in the literature of causality (Pearl, 2000) and Bayesian nets (Lauritzen, 1996). We compare the analysis of causality based on a methodology inspired by Haavelmo’s ideas with other approaches used in the causal literature of DAGs. We discuss the limitations of methods that solely use the information expressed in DAGs for the identification of economic models. We extend our framework to consider models for simultaneous causality, a central contribution of Haavelmo.

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Adjustment for time-invariant and time-varying confounders in ‘unexplained residuals’ models for longitudinal data within a causal framework and associated challenges

Statistical Methods in Medical Research ◽

10.1177/0962280218756158 ◽

2018 ◽

Vol 28 (5) ◽

pp. 1347-1364 ◽

Cited By ~ 1

Author(s):

KF Arnold ◽

GTH Ellison ◽

SC Gadd ◽

J Textor ◽

PWG Tennant ◽

...

Keyword(s):

Directed Acyclic Graphs ◽

Regression Methods ◽

Modelling Framework ◽

Acyclic Graphs ◽

Confounding Variables ◽

Confounder Adjustment ◽

Causal Framework ◽

Distal Outcome ◽

Time Invariant ◽

Insight Into

‘Unexplained residuals’ models have been used within lifecourse epidemiology to model an exposure measured longitudinally at several time points in relation to a distal outcome. It has been claimed that these models have several advantages, including: the ability to estimate multiple total causal effects in a single model, and additional insight into the effect on the outcome of greater-than-expected increases in the exposure compared to traditional regression methods. We evaluate these properties and prove mathematically how adjustment for confounding variables must be made within this modelling framework. Importantly, we explicitly place unexplained residual models in a causal framework using directed acyclic graphs. This allows for theoretical justification of appropriate confounder adjustment and provides a framework for extending our results to more complex scenarios than those examined in this paper. We also discuss several interpretational issues relating to unexplained residual models within a causal framework. We argue that unexplained residual models offer no additional insights compared to traditional regression methods, and, in fact, are more challenging to implement; moreover, they artificially reduce estimated standard errors. Consequently, we conclude that unexplained residual models, if used, must be implemented with great care.

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Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations

International Journal of Epidemiology ◽

10.1093/ije/dyaa213 ◽

2020 ◽

Author(s):

Peter W G Tennant ◽

Eleanor J Murray ◽

Kellyn F Arnold ◽

Laurie Berrie ◽

Matthew P Fox ◽

...

Keyword(s):

Health Research ◽

Web Of Science ◽

Directed Acyclic Graphs ◽

Causal Effects ◽

Research Review ◽

Future Research ◽

Substantial Variation ◽

Acyclic Graphs ◽

Confounding Variables ◽

Applied Health Research

Abstract Background Directed acyclic graphs (DAGs) are an increasingly popular approach for identifying confounding variables that require conditioning when estimating causal effects. This review examined the use of DAGs in applied health research to inform recommendations for improving their transparency and utility in future research. Methods Original health research articles published during 1999–2017 mentioning ‘directed acyclic graphs’ (or similar) or citing DAGitty were identified from Scopus, Web of Science, Medline and Embase. Data were extracted on the reporting of: estimands, DAGs and adjustment sets, alongside the characteristics of each article’s largest DAG. Results A total of 234 articles were identified that reported using DAGs. A fifth (n = 48, 21%) reported their target estimand(s) and half (n = 115, 48%) reported the adjustment set(s) implied by their DAG(s). Two-thirds of the articles (n = 144, 62%) made at least one DAG available. DAGs varied in size but averaged 12 nodes [interquartile range (IQR): 9–16, range: 3–28] and 29 arcs (IQR: 19–42, range: 3–99). The median saturation (i.e. percentage of total possible arcs) was 46% (IQR: 31–67, range: 12–100). 37% (n = 53) of the DAGs included unobserved variables, 17% (n = 25) included ‘super-nodes’ (i.e. nodes containing more than one variable) and 34% (n = 49) were visually arranged so that the constituent arcs flowed in the same direction (e.g. top-to-bottom). Conclusion There is substantial variation in the use and reporting of DAGs in applied health research. Although this partly reflects their flexibility, it also highlights some potential areas for improvement. This review hence offers several recommendations to improve the reporting and use of DAGs in future research.

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Modeling overlapping structures

Proceedings of Balisage: The Markup Conference 2013 ◽

10.4242/balisagevol10.marcoux01 ◽

2013 ◽

Cited By ~ 7

Author(s):

Yves Marcoux ◽

Michael Sperberg-McQueen ◽

Claus Huitfeldt

Keyword(s):

Directed Graphs ◽

Upper Bounds ◽

Directed Acyclic Graphs ◽

Markup Language ◽

Multiple Roots ◽

Acyclic Graph ◽

Xml Documents ◽

Acyclic Graphs ◽

Structured Document ◽

Definition Of

The problem of overlapping structures has long been familiar to the structured document community. In a poem, for example, the verse and line structures overlap, and having them both available simultaneously is convenient, and sometimes necessary (for example for automatic analyses). However, only structures that embed nicely can be represented directly in XML. Proposals to address this problem include XML solutions (based essentially on a layer of semantics) and non-XML ones. Among the latter is TexMecs HS2003, a markup language that allows overlap (and many other features). XML documents, when viewed as graphs, correspond to trees. Marcoux M2008 characterized overlap-only TexMecs documents by showing that they correspond exactly to completion-acyclic node-ordered directed acyclic graphs. In this paper, we elaborate on that result in two ways. First, we cast it in the setting of a strictly larger class of graphs, child-arc-ordered directed graphs, that includes multi-graphs and non-acyclic graphs, and show that — somewhat surprisingly — it does not hold in general for graphs with multiple roots. Second, we formulate a stronger condition, full-completion-acyclicity, that guarantees correspondence with an overlap-only document, even for graphs that have multiple roots. The definition of fully-completion-acyclic graph does not in itself suggest an efficient algorithm for checking the condition, nor for computing a corresponding overlap-only document when the condition is satisfied. We present basic polynomial-time upper bounds on the complexity of accomplishing those tasks.

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Use of directed acyclic graphs (DAGs) in applied health research: review and recommendations

10.1101/2019.12.20.19015511 ◽

2019 ◽

Cited By ~ 7

Author(s):

Peter WG Tennant ◽

Wendy J Harrison ◽

Eleanor J Murray ◽

Kellyn F Arnold ◽

Laurie Berrie ◽

...

Keyword(s):

Health Research ◽

Web Of Science ◽

Directed Acyclic Graphs ◽

Causal Effects ◽

Research Review ◽

Future Research ◽

Acyclic Graphs ◽

Confounding Variables ◽

Applied Health Research ◽

Consistent Direction

ABSTRACTBackgroundDirected acyclic graphs (DAGs) are an increasingly popular approach for identifying confounding variables that require adjustment when estimating causal effects. This review examined the use of DAGs in applied health research to inform recommendations for improving their transparency and utility in future research.MethodsOriginal health research articles published during 1999-2017 mentioning “directed acyclic graphs” or similar or citing DAGitty were identified from Scopus, Web of Science, Medline, and Embase. Data were extracted on the reporting of: estimands, DAGs, and adjustment sets, alongside the characteristics of each article’s largest DAG.ResultsA total of 234 articles were identified that reported using DAGs. A fifth (n=48, 21%) reported their target estimand(s) and half (n=115, 48%) reported the adjustment set(s) implied by their DAG(s).Two-thirds of the articles (n=144, 62%) made at least one DAG available. Diagrams varied in size but averaged 12 nodes (IQR: 9-16, range: 3-28) and 29 arcs (IQR: 19-42, range: 3-99). The median saturation (i.e. percentage of total possible arcs) was 46% (IQR: 31-67, range: 12-100). 37% (n=53) of the DAGs included unobserved variables, 17% (n=25) included super-nodes (i.e. nodes containing more than one variable, and a 34% (n=49) were arranged so the constituent arcs flowed in a consistent direction.ConclusionsThere is substantial variation in the use and reporting of DAGs in applied health research. Although this partly reflects their flexibility, it also highlight some potential areas for improvement. This review hence offers several recommendations to improve the reporting and use of DAGs in future research.

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Mathematical modeling of directed acyclic graphs to explore competing causal mechanisms underlying epidemiological study data

10.1101/19007922 ◽

2019 ◽

Author(s):

Joshua Havumaki ◽

Marisa C. Eisenberg

Keyword(s):

Epidemiological Study ◽

Study Design ◽

Simulated Data ◽

Population Level ◽

Obesity Paradox ◽

Epidemiological Studies ◽

Directed Acyclic Graphs ◽

Causal Mechanisms ◽

Acyclic Graphs ◽

Study Results

1AbstractAccurately estimating the effect of an exposure on an outcome requires understanding how variables relevant to a study question are causally related to each other. Directed acyclic graphs (DAGs) are used in epidemiology to understand causal processes and determine appropriate statistical approaches to obtain unbiased measures of effect. Compartmental models (CMs) are also used to represent different causal mechanisms, by depicting flows between disease states on the population level. In this paper, we extend a mapping between DAGs and CMs to show how DAG–derived CMs can be used to compare competing causal mechanisms by simulating epidemiological studies and conducting statistical analyses on the simulated data. Through this framework, we can evaluate how robust simulated epidemiological study results are to different biases in study design and underlying causal mechanisms. As a case study, we simulated a longitudinal cohort study to examine the obesity paradox: the apparent protective effect of obesity on mortality among diabetic ever-smokers, but not among diabetic never-smokers. Our simulations illustrate how study design bias (e.g., reverse causation), can lead to the obesity paradox. Ultimately, we show the utility of transforming DAGs into in silico laboratories within which researchers can systematically evaluate bias, and inform analyses and study design.

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Links between psychotic and neurotic symptoms in the general population: an analysis of longitudinal British National Survey data using Directed Acyclic Graphs

Psychological Medicine ◽

10.1017/s0033291718000879 ◽

2018 ◽

Vol 49 (3) ◽

pp. 388-395 ◽

Cited By ~ 11

Author(s):

J. Kuipers ◽

G. Moffa ◽

E. Kuipers ◽

D. Freeman ◽

P. Bebbington

Keyword(s):

Sleep Problems ◽

Psychiatric Morbidity ◽

Dynamic Bayesian Network ◽

Epidemiological Studies ◽

Directed Acyclic Graphs ◽

The Other ◽

Dependence Structure ◽

Affective Symptoms ◽

Acyclic Graphs ◽

Multiple Variables

AbstractBackgroundNon-psychotic affective symptoms are important components of psychotic syndromes. They are frequent and are now thought to influence the emergence of paranoia and hallucinations. Evidence supporting this model of psychosis comes from recent cross-fertilising epidemiological and intervention studies. Epidemiological studies identify plausible targets for intervention but must be interpreted cautiously. Nevertheless, causal inference can be strengthened substantially using modern statistical methods.MethodsDirected Acyclic Graphs were used in a dynamic Bayesian network approach to learn the overall dependence structure of chosen variables. DAG-based inference identifies the most likely directional links between multiple variables, thereby locating them in a putative causal cascade. We used initial and 18-month follow-up data from the 2000 British National Psychiatric Morbidity survey (N = 8580 and N = 2406).ResultsWe analysed persecutory ideation, hallucinations, a range of affective symptoms and the effects of cannabis and problematic alcohol use. Worry was central to the links between symptoms, with plausible direct effects on insomnia, depressed mood and generalised anxiety, and recent cannabis use. Worry linked the other affective phenomena with paranoia. Hallucinations were connected only to worry and persecutory ideation. General anxiety, worry, sleep problems, and persecutory ideation were strongly self-predicting. Worry and persecutory ideation were connected over the 18-month interval in an apparent feedback loop.ConclusionsThese results have implications for understanding dynamic processes in psychosis and for targeting psychological interventions. The reciprocal influence of worry and paranoia implies that treating either symptom is likely to ameliorate the other.

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Maternal smoking during pregnancy and early development of overweight and growth deficit in children: an analysis of survival

Revista Brasileira de Saúde Materno Infantil ◽

10.1590/1806-93042018000200007 ◽

2018 ◽

Vol 18 (2) ◽

pp. 361-369

Author(s):

Poliana Cristina de Almeida Fonseca ◽

Carolina Abreu de Carvalho ◽

Vitória Abreu de Carvalho ◽

Andréia Queiroz Ribeiro ◽

Silvia Eloiza Priore ◽

...

Keyword(s):

Maternal Smoking ◽

Cox Regression ◽

Directed Acyclic Graphs ◽

Z Score ◽

Smoking During Pregnancy ◽

Acyclic Graphs ◽

Confounding Variables ◽

Maternal Smoking During Pregnancy ◽

Pregnancy Smoking ◽

Growth Deficit

Abstract Objectives: to evaluate the association between smoking during pregnancy and nutritional status. Methods: cohort study with a sample of 460 children in the baseline. The children were assessed four times, being measured for weight and length to be converted in indexes length forage (L/A) and body mass index forage (BMI/A) in Z-score. The time until occurrence of growth deficit and overweight was calculated in days and compared to maternal smoking during pregnancy. To assess the association between smoking during pregnancy and the outcomes, a Hazard Ratio by Cox regression was obtained, adjusting by confounding variables selected from Directed Acyclic Graphs (DAG). Results: the time until occurrence of growth deficit and overweight was lower in children whose mothers smoked during pregnancy. Smoking during pregnancy was a risk factor for length deficit (HR = 2.84; CI95% = 1.42 to 5.70) and for overweight (HR = 1.96; CI95% = 1, 09 to 3.53), even after the adjustment. Conclusions: maternal smoking was a changeable factor associated with anthropometric outcomes, which demonstrates the need for actions to combat smoking during pregnancy in order to prevent early nutritional deviations.

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A biologist's guide to model selection and causal inference

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2020.2815 ◽

2021 ◽

Vol 288 (1943) ◽

pp. 20202815

Author(s):

Zachary M. Laubach ◽

Eleanor J. Murray ◽

Kim L. Hoke ◽

Rebecca J. Safran ◽

Wei Perng

Keyword(s):

Data Analysis ◽

Causal Inference ◽

Analytical Framework ◽

Directed Acyclic Graphs ◽

Biological Processes ◽

Large Complex ◽

Acyclic Graphs ◽

Research Programmes ◽

And Behavior

A goal of many research programmes in biology is to extract meaningful insights from large, complex datasets. Researchers in ecology, evolution and behavior (EEB) often grapple with long-term, observational datasets from which they construct models to test causal hypotheses about biological processes. Similarly, epidemiologists analyse large, complex observational datasets to understand the distribution and determinants of human health. A key difference in the analytical workflows for these two distinct areas of biology is the delineation of data analysis tasks and explicit use of causal directed acyclic graphs (DAGs), widely adopted by epidemiologists. Here, we review the most recent causal inference literature and describe an analytical workflow that has direct applications for EEB. We start this commentary by defining four distinct analytical tasks (description, prediction, association, causal inference). The remainder of the text is dedicated to causal inference, specifically focusing on the use of DAGs to inform the modelling strategy. Given the increasing interest in causal inference and misperceptions regarding this task, we seek to facilitate an exchange of ideas between disciplinary silos and provide an analytical framework that is particularly relevant for making causal inference from observational data.

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